tbuckley/cabot-search huggingface dataset:
Oncologist-driven development of an electronic health record (EHR) clinical data visualization tool for prostate cancer.
Authors: Teja Ganta, Jonathan Forrest Anker, Eric James Miller, Joseph Kannry, Aarti Sonia Bhardwaj, Kai Tsao, William K. Oh
Journal: Journal of Clinical Oncology (2023)

267 Background: Clinical information management is a burdensome process for oncology providers owing to the complexity of modern cancer data. The results that a clinician needs to review are often spread across several areas within the EHR, making it cumbersome to sense a broad overview of patient status. The purpose of this study is to describe a framework for oncologist-driven development of a data visualization tool to trend cancer biomarkers and assess the feasibility of this tool to query and display EHR data relevant for the treatment of prostate cancer. Methods: A clinical sponsor is selected to identify data elements necessary to make treatment decisions for patients receiving therapy for prostate cancer and to provide rapid feedback for the clinical tool interface. The commercial EHR database is queried to determine identifying codes for relevant laboratory tests, medications, and procedures. Data elements are assembled using the EHR platform for clinical synopsis and the clinical tool is made available to five genitourinary medical oncologists for initial pilot. Results: Oral prostate cancer medications were queried based on medication therapeutic and pharmaceutical class. In addition, androgen deprivation therapy (ADT) injections were separately identified based on route of administration. Prostate specific antigen (PSA) and testosterone result values were queried using laboratory base and/or common name codes. However, many duplicate entities are found varying by hospital/laboratory site and test assay. The assembled clinical data visualization tool can overlay temporal trends in PSA and testosterone over medication start/stop dates to convey treatment response and signs of early medication resistance. The clinician also has the option to overlay vital signs or other laboratory information to visualize treatment related adverse events (ex. weight gain related to ADT, anemia related to PARP inhibitor therapy, etc.) Lastly, the tool can also highlight the dates of the patient’s last imaging tests to allow clinicians to determine if the patient is due for any follow up imaging. Conclusions: The EHR can support novel data visualization tools for cancer biomarkers that can reasonably support clinical workflows. Development requires an intimate knowledge of EHR data but may still be limited by duplicate or erroneous codes for laboratory results. This issue may be addressed by using standard nomenclature for laboratory results such as LOINC codes but is not currently supported by the commercial EHR. Future work in this area will include formal usability testing from the perspective of oncology providers and patients.
Disparities in prostate cancer survival outcomes in Appalachian Kentucky.
Authors: Deepali Pandey, Quan Chen, Bin Huang, Zin Myint, Peng Wang
Journal: Journal of Clinical Oncology (2023)

39 Background: Prostate cancer (PC) is the most common male cancer in the United States and the second leading cause of cancer-related death. Our group previously reported that men from Appalachian Kentucky had worse survival outcomes compared to non-Appalachian Kentucky and general population. This survival difference could be mainly due to socio-economic disparities. However, several new treatments have been approved for metastatic PC (mPC) in the last decade and these current standard of care (SOC) therapies have significantly improved survival outcomes in mPC. Therefore, we aim to investigate whether the recent change in SOC treatments have improved overall survival (OS) in patients with Appalachian (App) Kentucky vs. Non-Appalachian (Non-App) Kentucky from 2004–2019. Methods: All male patients who were &gt; 20 years old with primary diagnoses of prostate adenocarcinoma, listed in Kentucky cancer Registry (KCR), diagnosed between 2004 and 2019, were included in the study. Basic demographics and survival outcomes were compared for all stages of PC and between App and Non-App Kentucky. We also compared the survival difference between two time periods 2004- 2011 vs. 2012-2019. Kaplan Meir and multivariate Cox Regression were performed to compare patients’ overall survival (OS). Results: A total of 36,695 patients were included in this retrospective analysis, of which 74.3% were Non-App and 15.7% were App. Caucasians predominated in both groups (N-App 85% vs. App 97%). The majority of Kentucky patients were aged between 50-74 years (79%). App Kentucky was noted to have more poorly differentiated histology (44.4% vs 40.1%), high Gleason score (26.4% vs 20%), more distant disease at the time of diagnosis (8.3 vs 6.7%), and higher percentage of patients with smoking history (40.1% vs 34.9%) compared to Non-App, respectively. A higher proportion of Non-App Kentucky had private insurance (35%) at the time of cancer diagnosis compared to more patients enrolled in Medicare (56.6%) or Medicaid (4.7%) in App. There was a survival difference between App and Non-App Kentucky by univariate analysis (p&lt;0.001). In multivariate Cox regression showed that Non-App patients had better OS (p&lt;0.001; HR=0.92, 95%CI 0.87-0.96). Patients diagnosed in the years 2012- 2019 had better survival compared to patients in 2004 – 2011 group (p=0.01; HR=0.94, 95%CI 0.9-0.99). Other factors that contributed to better survival include younger age, lived in the metro area, had private insurance, being a nonsmoker, being lower stage, and receiving treatment. We got similar result for stage IV patients, but the App variable was not significant in the COX regression (p=0.2). Conclusions: Our study shows that Appalachian has worse outcomes compared than Non-Appalachian Kentucky based on population-based analysis from 2004-2019. Additional research is needed to understand the health care disparities in App Kentucky.
Nivolumab plus ipilimumab for the treatment of post-chemotherapy metastatic castration-resistant prostate cancer (mCRPC): Additional results from the randomized phase 2 CheckMate 650 trial.
Authors: Padmanee Sharma, Michael Krainer, Fred Saad, Daniel Castellano, Jens Bedke, Mariusz Kwiatkowski, Akash Patnaik, Giuseppe Procopio, Pawel Wiechno, Samith Thomas Kochuparambil, Christian Thomas, Jose Angel Arranz Arija, Steven L. McCune, Steinbjorn Hansen, Gedske Daugaard, Neha P. Amin, Yumeng Wang, Justin M. David, Russell Kent Pachynski
Journal: Journal of Clinical Oncology (2023)

22 Background: In preliminary analyses from the randomized phase 2, open-label CheckMate 650 trial, nivolumab (NIVO) 1 mg/kg (N1) plus ipilimumab (IPI) 3 mg/kg (I3) Q3W × 4 doses showed clinical activity in patients (pts) with post-chemotherapy (post-CT) mCRPC, particularly those with high tumor mutational burden (TMB), but early toxicity contributed to treatment discontinuations. Here, we report results from pts with post-CT mCRPC receiving alternative NIVO+IPI dosing regimens vs IPI alone vs cabazitaxel (CABA) in CheckMate 650. Methods: Newly enrolled pts previously treated with docetaxel for mCRPC were randomized 2:2:1:2 to cohorts D1 (NIVO 3 mg/kg [N3] + IPI 1 mg/kg [I1] Q3W × 4 doses then NIVO 480 mg Q4W), D2 (N1 Q3W × 8 doses + I3 Q6W × 4 doses then NIVO 480 mg Q4W), D3 (I3 Q3W × 4 doses), or D4 (CABA 20 or 25 mg/m 2 Q3W + prednisone 10 mg × 10 doses). Crossover from cohorts D3 and D4 to cohort D1 was allowed after radiographic progressive disease. Outcomes included safety, objective response rate (ORR), prostate-specific antigen response rate (PSA-RR; confirmed PSA decline ≥ 50% from baseline), radiographic progression-free survival (rPFS) per blinded independent central review (BICR), and overall survival (OS). Associations between efficacy and TMB were assessed. Results: Overall, 259 pts were randomized (D1, n = 73; D2, n = 74; D3, n = 38; D4, n = 74). Median (range) follow-up for OS was 23.3 (6.0–31.5) months. In the NIVO+IPI cohorts (D1 and D2), median duration of therapy was 2.8 and 2.4 months; the median number of IPI doses received was 4 and 2, and 15% and 26% of pts discontinued due to study drug toxicity, respectively. Two pts died due to study drug toxicity (1 each in cohorts D1 and D2). Key safety and efficacy data are shown. Several pts in cohorts D1 and D2 showed notable reductions (75-100%) in tumor size and PSA. Approximately one-third of pts in cohorts D3 and D4 crossed over to D1. Based on preliminary analyses in small numbers of evaluable pts, there was no clear and consistent association between efficacy and tissue or blood TMB in the NIVO+IPI cohorts (D1 and D2). Conclusions: These results further support the clinical activity of NIVO+IPI in select pts with post-CT mCRPC. Detailed evaluations of the characteristics of responders to NIVO+IPI, including more expansive biomarker analyses, are warranted. Clinical trial information: NCT02985957 . [Table: see text]
Development and validation of a novel radiomic model for predicting post-treatment prostate cancer recurrence.
Authors: Linda My Huynh, Olivia Taylor, Shuo Wang, Michael Baine
Journal: Journal of Clinical Oncology (2023)

317 Background: Multiparametric magnetic resonance imaging (mpMRI)-derived radiomic features have been shown to capture sub-visual patterns for quantitative characterization of tumor phenotypes in prostate cancer (PC) patients. The present study seeks to develop, test, and validate a mpMRI-derived radiomic model for predicting PC recurrence following initial treatment. Methods: mpMRI was obtained from 76 patients who had undergone radical prostatectomy for treatment of localized PC. All patients had &gt;2 years follow-up with monitoring via prostate specific antigen (PSA). Patients with neo-adjuvant/adjuvant treatment were excluded. Regions of interest were manually delineated on each mpMRI and radiomics features were extracted. Stability on histogram matching was assessed via interclass correlation coefficients (ICC) and a pre-established feature selection pipeline excluded redundant features. The most important and non-redundant features were then aggregated into a radiomic model against a primary outcome of PC recurrence. Patients were randomly split 3 to 1 to training and validation datasets and the training dataset was used to iteratively train and test the best parameters for a Random Forest predictive model. The model with the best parameters as determined by receiver-operator curve (ROC) analysis was then applied to the validation set. ROC analysis was conducted and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were reported. Results: Clinicodemographic of the 76 patients included in analysis are displayed in Table. 924 radiomic features were initially extracted and 75 (8.1%) features were determined to be stable and robust following histogram matching. Based on the figure selection pipeline, six features were determined to be important and non-redundant. These features were iteratively tested in the training dataset (n=56) and the model with the best parameters yielded a mean ROC with area under the curve (AUC) of 0.95 ± 0.06. After application to validation dataset (n=20), the model yielded an AUC of 0.67. Sensitivity, specificity, PPV, and NPV were 33%, 100%, 40% and 100%, respectively. Conclusions: Radiomic analysis of 76 PC patients yielded six mpMRI-derived radiomic features significantly correlated with PC recurrence following primary treatment. When aggregated and applied to a validation dataset, the final radiomic model yielded 100% specificity in predicting PC recurrence. These findings represent excellent potential for the development of a radiomic-based diagnostic tool with high positive predictive value in identifying patients at high-risk for PC recurrence at time of initial PC diagnosis. Future projects will seek to incorporate patient demographics and disease characteristics to further improve model sensitivity and validation of this model will be pursued with an external cohort of patients.[Table: see text]
Association of biomarkers and outcomes in patients (pts) with metastatic urothelial carcinoma (mUC) treated with immune checkpoint inhibitors (ICIs).
Authors: Kevin R Reyes, Li Zhang, Xiaolin Zhu, Tanya Jindal, Prianka Deshmukh, Ivan de Kouchkovsky, Vipul Kumar, Edward Maldonado, Daniel H Kwon, Emily Chan, Sima P. Porten, Hala Borno, Rohit Bose, Arpita Desai, Rahul Raj Aggarwal, Eric Jay Small, Lawrence Fong, Jonathan Chou, Terence W. Friedlander, Vadim S Koshkin
Journal: Journal of Clinical Oncology (2023)

532 Background: ICIs form the backbone of treatment for mUC. However, only a minority of pts benefit and additional biomarkers of ICI response are needed. Methods: In our institution, we identified mUC pts treated with ICI monotherapy who had next generation sequencing (NGS). Somatic alterations present in ≥10% of pts ( ARID1A, CCND1, CDKN2A, CDKN2B, ERBB2, FGF3, FGF4, FGF19, FGFR3, KDM6A, MDM2, MLL2, PIK3CA, RB1, TERTp, TP53, TSC1), as well as DNA-damage response (DDR) alterations and tumor mutational burden (TMB) were analyzed as biomarkers. These biomarkers were individually evaluated in separate multivariate models while accounting for clinical factors including age, BMI, ECOG PS, primary tumor location, histology, hemoglobin, neutrophil to lymphocyte ratio and albumin. Multivariate cox regression and logistic regression models were used to measure hazard ratios (HR) and odds ratios (OR) for overall survival (OS), progression-free survival (PFS) and observed response rate (ORR). Results: Among 152 mUC ICI-treated pts, 107 had NGS data (FoundationOne, UCSF500, Strata), including 85 with TMB data. For the 107 pts with NGS, median age was 70 yrs, majority were male (69, 64%), Caucasian (70, 65%), had pure urothelial histology (57, 53%), and had first-line ICI (55, 51%). ORR was 35%, median PFS was 3.9 mos (95% CI: 2.6-7.5 mos), and median OS was 17.4 mos (95% CI: 14.1-30.6 mos). Biomarkers associated with improved outcomes to ICI, independent of relevant clinical factors, included alterations in ARID1A and DDR, as well as high TMB (&gt;10 Mut/Mb). Inferior outcomes were seen in pts with CDKN2B, KDM6A, FGF3, FGF4, and FGF19 alterations (Table). Conclusions: In this large retrospective multivariate analysis controlling for clinical factors in ICI-treated mUC pts, we found multiple biomarkers associated with improved or inferior outcomes. These hypothesis-generating findings can inform clinical decision making and trial design for mUC pts treated with ICIs, and should be validated in larger cohorts. [Table: see text]
Peripheral blood immune cell profiling in survivors of testicular germ cell tumors.
Authors: Michal Chovanec, Andrea Mlcakova, Zuzana Sestakova, Katarina Kalavska, Jana Obertova, Patrik Palacka, Katarina Rejlekova, Zuzana Sycova, Valentina De Angelis, Zuzana Orszaghova, Peter Lesko, Kristina Sekaninova, Daniela Svetlovska, Beata Mladosievicova, Jozef Mardiak, Michal Mego
Journal: Journal of Clinical Oncology (2023)

417 Background: Testicular germ cell tumors (GCT) achieve exceptional cure rate with cisplatin-based chemotherapy. Survivors of GCTs represent a unique population to study post-cancer treatment physiology and late toxicities. The impact of cancer treatment on the immune-cell profile in long-term GCT survivors is unknown. In this study, we performed an immune-phenotyping in survivors of GCTs. Methods: Whole peripheral blood was obtained from GCT survivors (N = 202) at National Cancer Institute of Slovakia within the protocol of the ongoing survivorship study on a day of their annual follow-up visit. The median follow-up was 11 years (2-25). GCT survivors were distributed into treatment groups: RT – radiotherapy to the retroperitoneum (N = 18), CT - chemotherapy (N = 143), CTRT - chemotherapy + radiotherapy (N=9); and a control group: AS - active surveillance/orchiectomy only (N=32). Immuno-phenotyping of peripheral blood leukocyte populations was performed with flow-cytometry. Immune cell subpopulations were statistically assessed for associations with received treatment. Results: Survivors treated with RT vs AS had higher no of classical dendritic cells (DCs) (mean ± SEM = 82.0 ± 1.9 vs 76.5 ±1.5, p = 0.03) and non-significantly lower no of plasmacytoid DCs and CD16+ DCs (0.11 ± 0.01 vs 0.15 ± 0.01, p = 0.06 and 54.4 ± 4.1 vs 64.8 ± 3.2, p = 0.07, respectively). Survivors treated with CT vs AS had higher no of CD19+ B cells (11.9 ± 0.3 vs 10.4 ± 0.7, p = 0.04)and lower no of CD8+ T cells (26.0 ± 1.3 vs 23.7 ± 0.6, p = 0.04). Similarly, survivors treated with CTRT vs AS had higher no of CD19+ B cells (13.6 ± 1.4 vs 10.4 ± 0.7, p = 0.04) and lower no of CD8+ T cells (20.7 ± 2.4 vs 26.0 ± 1.3, p = 0.04). Survivors treated with ≥ 400mg/m2 of cisplatin-based chemotherapy vs AS had no of CD19+ B cells (12.2 ± 0.5 vs 10.4 ± 0.7, p = 0.04) and lower no of CD8+ T cells (23.4 ± 0.9 vs 26.3 ±1.3, p = 0.04). The immunoregulatory index CD4/CD8 was higher in CTRT vs AS (2.5 ± 0.3 vs 1.8 ± 0.1, p = 0.04). Conclusions: Certain subpopulations of leukocytes differ according to received treatment in survivors of GCTs. Our results may suggest that chemotherapy and/or radiotherapy may produce long-term immunomodulatory effects. Interplay between B and T cells may be a contributing mechanism of late toxicities. Further research is needed to uncover the causal relationship to the long-term health of GCT survivors.
Immune checkpoint inhibitors in the peri-operative setting in renal cell carcinoma: A meta-analysis of the randomized clinical trials.
Authors: Jorge Esteban Villarrubia, Lucía Carril-Ajuria, Alberto Carretero-González, Carmen Romero Ferreiro, Pablo Álvarez, Maria Ruiz-Vico, Daniel Castellano, Guillermo de Velasco
Journal: Journal of Clinical Oncology (2023)

682 Background: Localized and oligometastatic resectable renal cell carcinoma (RCC) have moderate to high rates of relapse after surgery and evidence regarding the efficacy of perioperative treatments is conflicting. The risk of relapse varies across studies and there is a need to define the benefit/risk of immune checkpoint inhibitors (ICI) in this setting to optimize their use. Methods: A meta-analysis of the randomized phase 3 trials of peri-operative treatment with anti-PD-1/PD-L1 agents or anti-PD1/antiCTL4 in combination was conducted. The primary outcomes of interest were disease-free survival (DFS) and overall survival (OS). Secondary outcomes of interest were the proportion of grade 3-4 adverse events (AE) according to CTCAE 5.0. Subgroup analyses were performed according to clinically relevant characteristics. Statistical heterogeneity assumption was evaluated by the χ²-based Cochran’s Q test and quantified with the I² statistic. All statistical analyses were performed using Stata software (StataCorp LLC), version 16. Results: Published results of 4 phase 3 trials were included, involving 3393 patients. 1695 patients received ICI (anti-PD1: 900 patients; anti-PD-L1 390 patients, antiPD1/anti-CTLA4 405 patients) while 1712 received no active treatment (1297 placebo, 415 observation). ICI did not show to increase DFS (Hazard ratio (HR) 0.84 95% CI 0.69-1.03 p 0.10) or OS (HR 0.73 95% CI 0.40-1.34 p 0.31). High-grade AEs were 2.6 times more frequent in the immunotherapy arm (OR 2.64 95% CI 1.54 – 4.68 p &lt; 0.001) while high grade treatment-related AEs were 8 times more frequent in the experimental arm (OR 8.60 95% CI 3.23-22.91 p &lt; 0.001). Interestingly, subgroup analyses showed statistically significant differences favoring the experimental arm in females (HR 0.72 95% CI 0.55-0.93 p 0.01), in patients with sarcomatoid differentiation (HR 0.59 95% CI 0.403-0.88 p 0.010) and PD-L1 positive tumors (HR 0.74 95% CI 0.61-0.90 p 0.003). No significant effect was found in patients according to age, type of nephrectomy (radical vs. partial), and stage (M1 without evidence of disease vs M0 patients). Conclusions: The introduction of anti-PD-(L)1 +/- anti-CTLA-4 agents in the adjuvant setting of RCC might benefit selected RCC patients, although with increased toxicity. Longer follow-up of the conducted studies is needed to establish a potential benefit in long-term survival.
Evaluation of eligibility criteria in advanced prostate cancer clinical trials based on ASCO-FCR recommendations.
Authors: Matthew I. Feng, Sweta R. Prajapati, Daniela V. Castro, Kyle O. Lee, Elyse H. Chan, Ishaan Sehgal, Jalen Patel, Anna O'Dell, Zeynep Busra Zengin, Xiaochen Li, Alex Chehrazi-Raffle, Nazli Dizman, Abhishek Tripathi, Adam Rock, Sandy Liu, Benjamin D. Mercier, Luis A Meza, Errol James Philip, Tanya B. Dorff, Sumanta Monty Pal
Journal: Journal of Clinical Oncology (2023)

34 Background: The American Society of Clinical Oncology (ASCO) and the Friends of Cancer Research (FCR) published a joint statement addressing clinical eligibility for characteristics lacking adequate representation in recent studies (Kim et al., Clin Cancer Res 2021). Herein, we aimed to identify the frequency of other potentially excessive exclusion criteria in the context of metastatic prostate cancer. Methods: The eligibility criteria of advanced prostate cancer studies with start dates between June 30, 2012 and June 30, 2022 were identified through Clinicaltrials.gov. MeSH (Medical Subject Headings) terms in our query were “(metastatic OR advanced OR stage IV OR unresectable) AND (prostate cancer OR prostate adenocarcinoma)”. Our study included trials conducted worldwide and examined patients aged 18 and over in phase I-III trials. To narrow the scope of study, trials including more than one cancer type (basket trials) as well as those focusing on radiation therapy were not included in the study. Descriptive statistics were used to determine the frequency of eligibility criteria while the Fisher’s exact test or chi-square test were performed to demonstrate association between type of treatment and exclusion criteria. Results: 265 of 699 (37.9%) clinical trials within the specified search had sufficiently available data for evaluation. Of these, 136 (51.3%), 51 (19.2%), 26 (9.8%), 20 (7.5%), 19 (7.2%), and 7 (2.6%) of trials investigated treatment with combination therapy, hormone therapy, targeted therapy, immunotherapy, radioligand therapy, and chemotherapy, respectively. The most common ASCO-FCR-cited exclusion criteria in the studies were brain metastases (161/265, 60.8 %), HIV positivity (123/265, 46.4%), HBV/HCV positivity (122/265, 46.0%), and concurrent malignancies (41/265, 16.6%). Statistically significant relationships were identified between type of treatment along with status of brain metastases, HIV, and HBV/HCV (p=0.011, &lt;0.001, and 0.001, respectively. Conclusions: This study represents the first effort to analyze this specific subset of exclusion criteria in relation to type of treatment in advanced prostate cancer. Attention to the ASCO-FCR consensus statement may maximize patient representation in future studies and increase generalizability of data. [Table: see text]